from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
import plotly.offline as py
from plotly.graph_objs import *
import pandas as pd
import math
py.init_notebook_mode()
my_cols=['c1','c2','c3','c4','c5','c6','c7','c8','c9','c10']
with open('../data/obj_pose-laser-radar-synthetic-input.txt') as f:
table_input = pd.read_table(f, sep='\t', header=None, names=my_cols, lineterminator='\n')
table_input
| c1 | c2 | c3 | c4 | c5 | c6 | c7 | c8 | c9 | c10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | L | 0.312243 | 0.580340 | 1.477010e+15 | 6.000000e-01 | 0.600000 | 5.199937 | 0.000000 | 0.000000e+00 | 6.911322e-03 |
| 1 | R | 1.014892 | 0.554329 | 4.892807e+00 | 1.477010e+15 | 0.859997 | 0.600045 | 5.199747 | 1.796856e-03 | 3.455661e-04 |
| 2 | L | 1.173848 | 0.481073 | 1.477010e+15 | 1.119984e+00 | 0.600225 | 5.199429 | 0.005390 | 1.036644e-03 | 2.072960e-02 |
| 3 | R | 1.047505 | 0.389240 | 4.511325e+00 | 1.477010e+15 | 1.379955 | 0.600629 | 5.198979 | 1.077814e-02 | 2.073124e-03 |
| 4 | L | 1.650626 | 0.624690 | 1.477010e+15 | 1.639904e+00 | 0.601347 | 5.198392 | 0.017960 | 3.454842e-03 | 3.453479e-02 |
| 5 | R | 1.698300 | 0.298280 | 5.209986e+00 | 1.477010e+15 | 1.899823 | 0.602470 | 5.197661 | 2.693234e-02 | 5.181582e-03 |
| 6 | L | 2.188824 | 0.648739 | 1.477010e+15 | 2.159704e+00 | 0.604085 | 5.196776 | 0.037693 | 7.253069e-03 | 4.831816e-02 |
| 7 | R | 2.044382 | 0.276002 | 5.043867e+00 | 1.477010e+15 | 2.419540 | 0.606284 | 5.195728 | 5.023894e-02 | 9.668977e-03 |
| 8 | L | 2.655256 | 0.665980 | 1.477010e+15 | 2.679323e+00 | 0.609155 | 5.194504 | 0.064565 | 1.242892e-02 | 6.207101e-02 |
| 9 | R | 2.990916 | 0.217668 | 5.191807e+00 | 1.477010e+15 | 2.939043 | 0.612786 | 5.193090 | 8.066803e-02 | 1.553247e-02 |
| 10 | L | 3.012223 | 0.637046 | 1.477010e+15 | 3.198690e+00 | 0.617267 | 5.191470 | 0.098541 | 1.897914e-02 | 7.578466e-02 |
| 11 | R | 3.593878 | 0.135452 | 5.161753e+00 | 1.477010e+15 | 3.458253 | 0.622686 | 5.189627 | 1.181798e-01 | 2.276837e-02 |
| 12 | L | 3.893650 | 0.311793 | 1.477010e+15 | 3.717722e+00 | 0.629131 | 5.187542 | 0.139576 | 2.689958e-02 | 8.945044e-02 |
| 13 | R | 4.255547 | 0.164840 | 5.433327e+00 | 1.477010e+15 | 3.977082 | 0.636689 | 5.185194 | 1.627238e-01 | 3.137210e-02 |
| 14 | L | 4.309346 | 0.578564 | 1.477010e+15 | 4.236322e+00 | 0.645449 | 5.182560 | 0.187614 | 3.618523e-02 | 1.030597e-01 |
| 15 | R | 4.670263 | 0.148180 | 5.120847e+00 | 1.477010e+15 | 4.495424 | 0.655498 | 5.179618 | 2.142382e-01 | 4.133822e-02 |
| 16 | L | 4.351431 | 0.899174 | 1.477010e+15 | 4.754374e+00 | 0.666921 | 5.176340 | 0.242587 | 4.683024e-02 | 1.166039e-01 |
| 17 | R | 5.251417 | 0.127164 | 4.825914e+00 | 1.477010e+15 | 5.013155 | 0.679804 | 5.172700 | 2.726487e-01 | 5.266044e-02 |
| 18 | L | 5.518935 | 0.648233 | 1.477010e+15 | 5.271746e+00 | 0.694234 | 5.168671 | 0.304413 | 5.882788e-02 | 1.300744e-01 |
| 19 | R | 5.267293 | 0.121683 | 5.423506e+00 | 1.477010e+15 | 5.530128 | 0.710295 | 5.164221 | 3.378677e-01 | 6.533161e-02 |
| 20 | L | 6.022003 | 0.708619 | 1.477010e+15 | 5.788279e+00 | 0.728071 | 5.159319 | 0.372999 | 7.217058e-02 | 1.434628e-01 |
| 21 | R | 5.905749 | 0.063300 | 4.879680e+00 | 1.477010e+15 | 6.046176 | 0.747646 | 5.153933 | 4.097925e-01 | 7.934372e-02 |
| 22 | L | 6.342486 | 0.948833 | 1.477010e+15 | 6.303794e+00 | 0.769103 | 5.148029 | 0.448233 | 8.684990e-02 | 1.567606e-01 |
| 23 | R | 6.673922 | 0.125614 | 5.006870e+00 | 1.477010e+15 | 6.561105 | 0.792523 | 5.141571 | 4.883049e-01 | 9.468793e-02 |
| 24 | L | 6.782143 | 0.714036 | 1.477010e+15 | 6.818081e+00 | 0.817988 | 5.134523 | 0.529990 | 1.028566e-01 | 1.699593e-01 |
| 25 | R | 7.318441 | 0.086292 | 4.649107e+00 | 1.477010e+15 | 7.074691 | 0.845578 | 5.126847 | 5.732691e-01 | 1.113545e-01 |
| 26 | L | 7.137350 | 0.957217 | 1.477010e+15 | 7.330903e+00 | 0.875372 | 5.118505 | 0.618123 | 1.201805e-01 | 1.830507e-01 |
| 27 | R | 8.124935 | 0.101047 | 5.464240e+00 | 1.477010e+15 | 7.586684 | 0.907449 | 5.109456 | 6.645307e-01 | 1.293330e-01 |
| 28 | L | 7.805334 | 0.719126 | 1.477010e+15 | 7.841995e+00 | 0.941886 | 5.099659 | 0.712469 | 1.388107e-01 | 1.960265e-01 |
| 29 | R | 8.450951 | 0.104862 | 4.750535e+00 | 1.477010e+15 | 8.096800 | 0.978758 | 5.089074 | 7.619151e-01 | 1.486120e-01 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 470 | L | -14.408830 | 10.222010 | 1.477010e+15 | -1.447663e+01 | 10.527600 | 5.092432 | 0.762418 | 1.486120e-01 | -1.960265e-01 |
| 471 | R | 17.434840 | 2.493576 | -3.802615e+00 | 1.477010e+15 | -14.221830 | 10.564470 | 5.102928 | 7.129259e-01 | 1.388107e-01 |
| 472 | L | -13.804860 | 10.938330 | 1.477010e+15 | -1.396652e+01 | 10.598910 | 5.112633 | 0.664944 | 1.293330e-01 | -1.830507e-01 |
| 473 | R | 17.198060 | 2.463710 | -3.313897e+00 | 1.477010e+15 | -13.710730 | 10.630990 | 5.121588 | 6.184955e-01 | 1.201805e-01 |
| 474 | L | -13.355600 | 10.551700 | 1.477010e+15 | -1.345452e+01 | 10.660780 | 5.129834 | 0.573603 | 1.113545e-01 | -1.699593e-01 |
| 475 | R | 16.861570 | 2.445369 | -3.743524e+00 | 1.477010e+15 | -13.197910 | 10.688370 | 5.137411 | 5.302878e-01 | 1.028566e-01 |
| 476 | L | -12.959060 | 11.009890 | 1.477010e+15 | -1.294094e+01 | 10.713830 | 5.144357 | 0.488570 | 9.468793e-02 | -1.567606e-01 |
| 477 | R | 16.633020 | 2.398815 | -3.455295e+00 | 1.477010e+15 | -12.683620 | 10.737260 | 5.150712 | 4.484670e-01 | 8.684990e-02 |
| 478 | L | -12.371330 | 10.609140 | 1.477010e+15 | -1.242601e+01 | 10.758710 | 5.156511 | 0.409998 | 7.934372e-02 | -1.434628e-01 |
| 479 | R | 16.174360 | 2.400763 | -3.361253e+00 | 1.477010e+15 | -12.168110 | 10.778290 | 5.161789 | 3.731774e-01 | 7.217058e-02 |
| 480 | L | -11.945090 | 11.051500 | 1.477010e+15 | -1.190996e+01 | 10.796060 | 5.166582 | 0.338022 | 6.533161e-02 | -1.300744e-01 |
| 481 | R | 15.512470 | 2.356317 | -3.456733e+00 | 1.477010e+15 | -11.651580 | 10.812120 | 5.170921 | 3.045457e-01 | 5.882788e-02 |
| 482 | L | -11.454830 | 10.940670 | 1.477010e+15 | -1.139299e+01 | 10.826550 | 5.174838 | 0.272761 | 5.266044e-02 | -1.166039e-01 |
| 483 | R | 15.467450 | 2.394427 | -4.449697e+00 | 1.477010e+15 | -11.134210 | 10.839440 | 5.178363 | 2.426814e-01 | 4.683024e-02 |
| 484 | L | -10.600860 | 10.682250 | 1.477010e+15 | -1.087526e+01 | 10.850860 | 5.181525 | 0.214317 | 4.133822e-02 | -1.030597e-01 |
| 485 | R | 15.198470 | 2.347791 | -3.728088e+00 | 1.477010e+15 | -10.616150 | 10.860910 | 5.184351 | 1.876789e-01 | 3.618523e-02 |
| 486 | L | -10.414230 | 10.874650 | 1.477010e+15 | -1.035691e+01 | 10.869670 | 5.186866 | 0.162776 | 3.137210e-02 | -8.945044e-02 |
| 487 | R | 14.970070 | 2.293741 | -2.909210e+00 | 1.477010e+15 | -10.097550 | 10.877230 | 5.189095 | 1.396181e-01 | 2.689958e-02 |
| 488 | L | -9.834446 | 11.103210 | 1.477010e+15 | -9.838085e+00 | 10.883670 | 5.191060 | 0.118212 | 2.276837e-02 | -7.578466e-02 |
| 489 | R | 14.527780 | 2.348663 | -3.348510e+00 | 1.477010e+15 | -9.578521 | 10.889090 | 5.192781 | 9.856635e-02 | 1.897914e-02 |
| 490 | L | -9.306602 | 11.034760 | 1.477010e+15 | -9.318874e+00 | 10.893570 | 5.194279 | 0.080686 | 1.553247e-02 | -6.207101e-02 |
| 491 | R | 13.864840 | 2.298530 | -3.203442e+00 | 1.477010e+15 | -9.059154 | 10.897200 | 5.195570 | 6.457867e-02 | 1.242892e-02 |
| 492 | L | -8.788986 | 11.070660 | 1.477010e+15 | -8.799372e+00 | 10.900070 | 5.196670 | 0.050248 | 9.668977e-03 | -4.831816e-02 |
| 493 | R | 13.370450 | 2.293074 | -3.215725e+00 | 1.477010e+15 | -8.539535 | 10.902270 | 5.197594 | 3.769917e-02 | 7.253069e-03 |
| 494 | L | -8.620445 | 10.657660 | 1.477010e+15 | -8.279654e+00 | 10.903890 | 5.198353 | 0.026936 | 5.181582e-03 | -3.453479e-02 |
| 495 | R | 13.645960 | 2.189595 | -2.987211e+00 | 1.477010e+15 | -8.019735 | 10.905010 | 5.198959 | 1.796166e-02 | 3.454842e-03 |
| 496 | L | -7.519712 | 11.000450 | 1.477010e+15 | -7.759787e+00 | 10.905730 | 5.199421 | 0.010779 | 2.073124e-03 | -2.072960e-02 |
| 497 | R | 12.885600 | 2.169303 | -2.779369e+00 | 1.477010e+15 | -7.499815 | 10.906130 | 5.199745 | 5.390285e-03 | 1.036644e-03 |
| 498 | L | -7.156314 | 10.815040 | 1.477010e+15 | -7.239828e+00 | 10.906310 | 5.199937 | 0.001797 | 3.455661e-04 | -6.911322e-03 |
| 499 | R | 13.269100 | 2.161844 | -2.405718e+00 | 1.477010e+15 | -6.979831 | 10.906360 | 5.200000 | -7.848735e-15 | -1.509372e-15 |
500 rows × 10 columns
import plotly.offline as py
from plotly.graph_objs import *
import pandas as pd
import math
py.init_notebook_mode()
my_cols=['sensor','px_est','py_est','vx_est','vy_est','px_meas','py_meas','px_gt','py_gt','vx_gt','vy_gt','NIS','RMSE x','RMSE y','RMSE vx','RMSE vy','acceleration_x','acceleration_y','dt']
with open('../data/obj_pose-laser-radar-ukf-output.txt') as f:
table_ukf_output = pd.read_table(f, sep='\t', header=None, names=my_cols, lineterminator='\n')
table_ukf_output
| sensor | px_est | py_est | vx_est | vy_est | px_meas | py_meas | px_gt | py_gt | vx_gt | vy_gt | NIS | RMSE x | RMSE y | RMSE vx | RMSE vy | acceleration_x | acceleration_y | dt | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | L | 0.312243 | 0.580340 | 5.000000 | 0.000000 | 0.312243 | 0.580340 | 0.600000 | 0.600000 | 5.199940 | 0.000000 | 0.000000 | 0.287757 | 0.019660 | 0.199937 | 0.000000 | 0.000000 | 0.000000 | 0.00 |
| 1 | R | 0.741466 | 0.499762 | 5.378667 | 0.134583 | 0.862916 | 0.534212 | 0.859997 | 0.600045 | 5.199747 | 0.001797 | 2.306294 | 0.220061 | 0.072261 | 0.189720 | 0.093894 | -0.003796 | 0.035937 | 0.05 |
| 2 | L | 1.059574 | 0.502753 | 5.640649 | -0.523301 | 1.173848 | 0.481073 | 1.119984 | 0.600225 | 5.199429 | 0.005390 | 0.923797 | 0.183033 | 0.081535 | 0.298140 | 0.314720 | -0.006361 | 0.071862 | 0.05 |
| 3 | R | 1.256753 | 0.513805 | 5.057700 | -0.075615 | 0.969149 | 0.397513 | 1.379955 | 0.600629 | 5.198979 | 0.010778 | 1.674388 | 0.170060 | 0.082889 | 0.267686 | 0.275957 | -0.009003 | 0.107764 | 0.05 |
| 4 | L | 1.552765 | 0.528905 | 5.145473 | -0.119564 | 1.650626 | 0.624690 | 1.639904 | 0.601347 | 5.198392 | 0.017960 | 1.119197 | 0.157019 | 0.080908 | 0.240592 | 0.254371 | -0.011740 | 0.143631 | 0.05 |
| 5 | R | 1.810837 | 0.546598 | 5.233300 | 0.198646 | 1.623309 | 0.499091 | 1.899823 | 0.602470 | 5.197661 | 0.026932 | 1.210026 | 0.147870 | 0.077300 | 0.220111 | 0.242559 | -0.014620 | 0.179453 | 0.05 |
| 6 | L | 2.098481 | 0.574277 | 5.264816 | 0.346316 | 2.188824 | 0.648739 | 2.159704 | 0.604085 | 5.196776 | 0.037693 | 0.777820 | 0.138843 | 0.072447 | 0.205399 | 0.253055 | -0.017700 | 0.215218 | 0.05 |
| 7 | R | 2.314849 | 0.632081 | 5.104791 | 0.787543 | 1.967008 | 0.557117 | 2.419540 | 0.606284 | 5.195728 | 0.050239 | 1.914186 | 0.135047 | 0.068379 | 0.194805 | 0.352114 | -0.020962 | 0.250914 | 0.05 |
| 8 | L | 2.586150 | 0.670757 | 5.118279 | 0.888579 | 2.655256 | 0.665980 | 2.679323 | 0.609155 | 5.194504 | 0.064565 | 0.272137 | 0.131057 | 0.067660 | 0.185413 | 0.430874 | -0.024481 | 0.286530 | 0.05 |
| 9 | R | 2.857759 | 0.667057 | 5.190672 | 0.667385 | 2.920341 | 0.645898 | 2.939043 | 0.612786 | 5.193090 | 0.080668 | 0.436781 | 0.126961 | 0.066443 | 0.175900 | 0.448900 | -0.028276 | 0.322052 | 0.05 |
| 10 | L | 3.096430 | 0.685079 | 5.166228 | 0.657451 | 3.012223 | 0.637046 | 3.198690 | 0.617267 | 5.191470 | 0.098541 | 0.523536 | 0.124917 | 0.066568 | 0.167887 | 0.459989 | -0.032396 | 0.357469 | 0.05 |
| 11 | R | 3.386724 | 0.582993 | 5.248301 | -0.119247 | 3.560959 | 0.485311 | 3.458253 | 0.622686 | 5.189627 | 0.118180 | 3.230599 | 0.121369 | 0.064756 | 0.161629 | 0.445707 | -0.036860 | 0.392767 | 0.05 |
| 12 | L | 3.693801 | 0.490449 | 5.268319 | -0.683098 | 3.893650 | 0.311793 | 3.717722 | 0.629131 | 5.187542 | 0.139576 | 4.283482 | 0.116796 | 0.073145 | 0.156896 | 0.485216 | -0.041704 | 0.427932 | 0.05 |
| 13 | R | 3.973959 | 0.560792 | 5.360751 | -0.206214 | 4.197862 | 0.698311 | 3.977082 | 0.636689 | 5.185194 | 0.162724 | 2.697666 | 0.112550 | 0.073345 | 0.158302 | 0.477850 | -0.046959 | 0.462948 | 0.05 |
| 14 | L | 4.251971 | 0.558035 | 5.374167 | -0.206539 | 4.309346 | 0.578564 | 4.236322 | 0.645449 | 5.182560 | 0.187614 | 0.201669 | 0.108809 | 0.074366 | 0.160737 | 0.472731 | -0.052681 | 0.497804 | 0.05 |
| 15 | R | 4.511635 | 0.591455 | 5.329997 | -0.042689 | 4.619083 | 0.689510 | 4.495424 | 0.655498 | 5.179618 | 0.214238 | 0.971787 | 0.105432 | 0.073763 | 0.160109 | 0.462205 | -0.058842 | 0.532484 | 0.05 |
| 16 | L | 4.705034 | 0.694538 | 5.198410 | 0.398556 | 4.351431 | 0.899174 | 4.754374 | 0.666921 | 5.176340 | 0.242587 | 9.512195 | 0.102982 | 0.071873 | 0.155421 | 0.449998 | -0.065556 | 0.566968 | 0.05 |
| 17 | R | 4.957839 | 0.690895 | 5.136764 | 0.329040 | 5.209015 | 0.665990 | 5.013155 | 0.679804 | 5.172700 | 0.272649 | 2.034607 | 0.100926 | 0.069897 | 0.151280 | 0.437521 | -0.072803 | 0.601242 | 0.05 |
| 18 | L | 5.262900 | 0.684604 | 5.208869 | 0.257305 | 5.518935 | 0.648233 | 5.271746 | 0.694234 | 5.168671 | 0.304413 | 3.567801 | 0.098255 | 0.068069 | 0.147533 | 0.425989 | -0.080576 | 0.635290 | 0.05 |
| 19 | R | 5.522686 | 0.690887 | 5.248742 | 0.249500 | 5.228345 | 0.639362 | 5.530128 | 0.710295 | 5.164221 | 0.337868 | 1.512094 | 0.095781 | 0.066487 | 0.145034 | 0.415672 | -0.089006 | 0.669090 | 0.05 |
| 20 | L | 5.821327 | 0.702001 | 5.298814 | 0.250180 | 6.022003 | 0.708619 | 5.788279 | 0.728071 | 5.159319 | 0.372999 | 2.120329 | 0.093751 | 0.065134 | 0.144775 | 0.406539 | -0.098038 | 0.702624 | 0.05 |
| 21 | R | 6.064652 | 0.634640 | 5.214659 | -0.000309 | 5.893921 | 0.373584 | 6.046176 | 0.747646 | 5.153933 | 0.409793 | 4.274659 | 0.091680 | 0.068044 | 0.142038 | 0.406702 | -0.107717 | 0.735872 | 0.05 |
| 22 | L | 6.323675 | 0.735937 | 5.201082 | 0.304900 | 6.342486 | 0.948833 | 6.303794 | 0.769103 | 5.148029 | 0.448233 | 2.990267 | 0.089761 | 0.066907 | 0.139356 | 0.398883 | -0.118084 | 0.768816 | 0.05 |
| 23 | R | 6.574407 | 0.766852 | 5.154574 | 0.362167 | 6.621337 | 0.836138 | 6.561105 | 0.792523 | 5.141571 | 0.488305 | 0.466965 | 0.087913 | 0.065708 | 0.136447 | 0.391333 | -0.129156 | 0.801432 | 0.05 |
| 24 | L | 6.825061 | 0.763275 | 5.148383 | 0.317124 | 6.782143 | 0.714036 | 6.818081 | 0.817988 | 5.134523 | 0.529990 | 0.250386 | 0.086148 | 0.065303 | 0.133719 | 0.385783 | -0.140963 | 0.833696 | 0.05 |
| 25 | R | 7.067966 | 0.750183 | 5.051952 | 0.246883 | 7.291210 | 0.630741 | 7.074691 | 0.845578 | 5.126847 | 0.573269 | 3.142548 | 0.084485 | 0.066712 | 0.131943 | 0.383668 | -0.153522 | 0.865588 | 0.05 |
| 26 | L | 7.289928 | 0.825731 | 5.000150 | 0.419245 | 7.137350 | 0.957217 | 7.330903 | 0.875372 | 5.118505 | 0.618123 | 2.376548 | 0.083280 | 0.066159 | 0.131464 | 0.378437 | -0.166836 | 0.897081 | 0.05 |
| 27 | R | 7.583298 | 0.838742 | 5.130212 | 0.418650 | 8.083490 | 0.819605 | 7.586684 | 0.907449 | 5.109456 | 0.664531 | 4.692094 | 0.081782 | 0.066251 | 0.129155 | 0.374511 | -0.180979 | 0.928150 | 0.05 |
| 28 | L | 7.836205 | 0.817478 | 5.129933 | 0.329613 | 7.805334 | 0.719126 | 7.841995 | 0.941886 | 5.099659 | 0.712469 | 0.662716 | 0.080367 | 0.069077 | 0.127033 | 0.374802 | -0.195942 | 0.958772 | 0.05 |
| 29 | R | 8.085638 | 0.836215 | 5.057846 | 0.340612 | 8.404531 | 0.884557 | 8.096800 | 0.978758 | 5.089074 | 0.761915 | 2.452605 | 0.079042 | 0.072731 | 0.125028 | 0.376445 | -0.211697 | 0.988916 | 0.05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 469 | R | -14.677060 | 10.427220 | 5.092820 | 0.610261 | -14.724010 | 9.635065 | -14.730890 | 10.488220 | 5.081101 | 0.813394 | 6.472394 | 0.073334 | 0.083481 | 0.179077 | 0.193616 | 0.244179 | -1.048685 | 0.05 |
| 470 | L | -14.434250 | 10.399390 | 5.065840 | 0.433725 | -14.408830 | 10.222010 | -14.476630 | 10.527600 | 5.092432 | 0.762418 | 1.858912 | 0.073282 | 0.083602 | 0.178891 | 0.194003 | 0.226622 | -1.019527 | 0.05 |
| 471 | R | -14.174870 | 10.423310 | 5.073041 | 0.361832 | -13.900490 | 10.523780 | -14.221830 | 10.564470 | 5.102928 | 0.712926 | 0.564942 | 0.073237 | 0.083766 | 0.178706 | 0.194470 | 0.209923 | -0.989835 | 0.05 |
| 472 | L | -13.867430 | 10.567220 | 5.196731 | 0.503086 | -13.804860 | 10.938330 | -13.966520 | 10.598910 | 5.112633 | 0.664944 | 8.551981 | 0.073301 | 0.083690 | 0.178559 | 0.194407 | 0.194101 | -0.959641 | 0.05 |
| 473 | R | -13.618620 | 10.619030 | 5.129890 | 0.509269 | -13.395610 | 10.785680 | -13.710730 | 10.630990 | 5.121588 | 0.618495 | 2.733645 | 0.073346 | 0.083603 | 0.178371 | 0.194266 | 0.179100 | -0.928967 | 0.05 |
| 474 | L | -13.366610 | 10.621580 | 5.123786 | 0.404472 | -13.355600 | 10.551700 | -13.454520 | 10.660780 | 5.129834 | 0.573603 | 0.286517 | 0.073379 | 0.083534 | 0.178184 | 0.194217 | 0.164919 | -0.897849 | 0.05 |
| 475 | R | -13.106140 | 10.646120 | 5.134246 | 0.347521 | -12.937370 | 10.813740 | -13.197910 | 10.688370 | 5.137411 | 0.530288 | 0.236242 | 0.073423 | 0.083469 | 0.177996 | 0.194193 | 0.151539 | -0.866306 | 0.05 |
| 476 | L | -12.849150 | 10.736700 | 5.158253 | 0.409999 | -12.959060 | 11.009890 | -12.940940 | 10.713830 | 5.144357 | 0.488570 | 4.760573 | 0.073466 | 0.083388 | 0.177811 | 0.194023 | 0.138922 | -0.834364 | 0.05 |
| 477 | R | -12.589040 | 10.782710 | 5.145145 | 0.409532 | -12.251760 | 11.249520 | -12.683620 | 10.737260 | 5.150712 | 0.448467 | 1.906165 | 0.073517 | 0.083327 | 0.177625 | 0.193828 | 0.127096 | -0.802052 | 0.05 |
| 478 | L | -12.352130 | 10.754690 | 5.106111 | 0.267607 | -12.371330 | 10.609140 | -12.426010 | 10.758710 | 5.156511 | 0.409998 | 1.280192 | 0.073517 | 0.083240 | 0.177454 | 0.193735 | 0.115976 | -0.769390 | 0.05 |
| 479 | R | -12.101170 | 10.785620 | 5.070662 | 0.257855 | -11.935200 | 10.916080 | -12.168110 | 10.778290 | 5.161789 | 0.373177 | 1.066952 | 0.073504 | 0.083154 | 0.177318 | 0.193605 | 0.105562 | -0.736402 | 0.05 |
| 480 | L | -11.851340 | 10.848830 | 5.080897 | 0.288161 | -11.945090 | 11.051500 | -11.909960 | 10.796060 | 5.166582 | 0.338022 | 2.696551 | 0.073477 | 0.083102 | 0.177177 | 0.193417 | 0.095863 | -0.703106 | 0.05 |
| 481 | R | -11.578180 | 10.868330 | 5.103909 | 0.253192 | -10.970320 | 10.967630 | -11.651580 | 10.812120 | 5.170921 | 0.304546 | 2.769714 | 0.073476 | 0.083055 | 0.177019 | 0.193230 | 0.086775 | -0.669528 | 0.05 |
| 482 | L | -11.346520 | 10.884970 | 5.071086 | 0.207081 | -11.454830 | 10.940670 | -11.392990 | 10.826550 | 5.174838 | 0.272761 | 0.784994 | 0.073431 | 0.083012 | 0.176899 | 0.193053 | 0.078344 | -0.635686 | 0.05 |
| 483 | R | -11.076970 | 10.834740 | 5.184942 | -0.013123 | -11.347200 | 10.511090 | -11.134210 | 10.839440 | 5.178363 | 0.242681 | 8.648595 | 0.073401 | 0.082926 | 0.176716 | 0.193204 | 0.070496 | -0.601600 | 0.05 |
| 484 | L | -10.781720 | 10.813400 | 5.234992 | -0.110717 | -10.600860 | 10.682250 | -10.875260 | 10.850860 | 5.181525 | 0.214317 | 2.620697 | 0.073448 | 0.082858 | 0.176551 | 0.193568 | 0.063248 | -0.567286 | 0.05 |
| 485 | R | -10.526370 | 10.809860 | 5.219823 | -0.168603 | -10.656250 | 10.836870 | -10.616150 | 10.860910 | 5.184351 | 0.187679 | 0.184757 | 0.073485 | 0.082806 | 0.176376 | 0.194043 | 0.056515 | -0.532764 | 0.05 |
| 486 | L | -10.292590 | 10.806590 | 5.176848 | -0.224080 | -10.414230 | 10.874650 | -10.356910 | 10.869670 | 5.186866 | 0.162776 | 1.029735 | 0.073468 | 0.082770 | 0.176196 | 0.194635 | 0.050297 | -0.498052 | 0.05 |
| 487 | R | -10.051240 | 10.849690 | 5.072957 | -0.136061 | -9.904133 | 11.225470 | -10.097550 | 10.877230 | 5.189095 | 0.139618 | 6.939873 | 0.073422 | 0.082694 | 0.176094 | 0.194835 | 0.044584 | -0.463164 | 0.05 |
| 488 | L | -9.789078 | 10.895110 | 5.099642 | -0.099600 | -9.834446 | 11.103210 | -9.838085 | 10.883670 | 5.191060 | 0.118212 | 2.493017 | 0.073381 | 0.082611 | 0.175962 | 0.194885 | 0.039301 | -0.428114 | 0.05 |
| 489 | R | -9.562141 | 10.875210 | 5.037344 | -0.166426 | -10.195030 | 10.349770 | -9.578521 | 10.889090 | 5.192781 | 0.098566 | 3.851388 | 0.073310 | 0.082529 | 0.175923 | 0.195054 | 0.034418 | -0.392921 | 0.05 |
| 490 | L | -9.299057 | 10.901240 | 5.063241 | -0.156556 | -9.306602 | 11.034760 | -9.318874 | 10.893570 | 5.194279 | 0.080686 | 1.001673 | 0.073240 | 0.082446 | 0.175843 | 0.195149 | 0.029964 | -0.357597 | 0.05 |
| 491 | R | -9.057308 | 10.880530 | 5.020085 | -0.209822 | -9.222603 | 10.352650 | -9.059154 | 10.897200 | 5.195570 | 0.064579 | 2.584013 | 0.073166 | 0.082366 | 0.175842 | 0.195343 | 0.025816 | -0.322156 | 0.05 |
| 492 | L | -8.790263 | 10.911200 | 5.055213 | -0.185163 | -8.788986 | 11.070660 | -8.799372 | 10.900070 | 5.196670 | 0.050248 | 1.430197 | 0.073093 | 0.082284 | 0.175779 | 0.195432 | 0.022001 | -0.286612 | 0.05 |
| 493 | R | -8.553499 | 10.869990 | 5.009641 | -0.274073 | -8.839141 | 10.031880 | -8.539535 | 10.902270 | 5.197594 | 0.037699 | 6.095158 | 0.073022 | 0.082213 | 0.175805 | 0.195738 | 0.018482 | -0.250978 | 0.05 |
| 494 | L | -8.386322 | 10.796910 | 4.854779 | -0.418184 | -8.620445 | 10.657660 | -8.279654 | 10.903890 | 5.198353 | 0.026936 | 4.524035 | 0.073105 | 0.082271 | 0.176305 | 0.196561 | 0.015173 | -0.215265 | 0.05 |
| 495 | R | -8.140694 | 10.803900 | 4.833407 | -0.397714 | -7.915434 | 11.115670 | -8.019735 | 10.905010 | 5.198959 | 0.017962 | 1.831414 | 0.073233 | 0.082313 | 0.176890 | 0.197247 | 0.012121 | -0.179485 | 0.05 |
| 496 | L | -7.800433 | 10.847610 | 5.007687 | -0.329395 | -7.519712 | 11.000450 | -7.759787 | 10.905730 | 5.199421 | 0.010779 | 6.212137 | 0.073182 | 0.082271 | 0.176921 | 0.197639 | 0.009241 | -0.143652 | 0.05 |
| 497 | R | -7.543596 | 10.844730 | 4.982779 | -0.311995 | -7.259867 | 10.645800 | -7.499815 | 10.906130 | 5.199745 | 0.005390 | 2.598313 | 0.073135 | 0.082235 | 0.177011 | 0.197952 | 0.006485 | -0.107776 | 0.05 |
| 498 | L | -7.263648 | 10.833610 | 5.032451 | -0.335603 | -7.156314 | 10.815040 | -7.239828 | 10.906310 | 5.199937 | 0.001797 | 0.670869 | 0.073069 | 0.082217 | 0.176992 | 0.198329 | 0.003834 | -0.071867 | 0.05 |
499 rows × 19 columns
import plotly.offline as py
from plotly.graph_objs import *
import pandas as pd
import math
py.init_notebook_mode()
my_cols=['sensor','px_est','py_est','vx_est','vy_est','px_meas','py_meas','px_gt','py_gt','vx_gt','vy_gt','NIS','RMSE x','RMSE y','RMSE vx','RMSE vy','acceleration_x','acceleration_y','dt']
with open('../data/obj_pose-laser-radar-ukf-output.txt') as f:
table_ukf_output = pd.read_table(f, sep='\t', header=None, names=my_cols, lineterminator='\n')
#table_ukf_output
import plotly.offline as py
from plotly.graph_objs import *
#Measurements
trace2 = Scatter(
x=table_ukf_output['px_meas'],
y=table_ukf_output['py_meas'],
xaxis='x2',
yaxis='y2',
name = 'Measurements',
#mode = 'markers'
)
#estimations
trace1 = Scatter(
x=table_ukf_output['px_est'],
y=table_ukf_output['py_est'],
xaxis='x2',
yaxis='y2',
name='UKF- Estimate',
mode = 'markers'
)
#Ground Truth
trace3 = Scatter(
x=table_ukf_output['px_gt'],
y=table_ukf_output['py_gt'],
xaxis='x2',
yaxis='y2',
name = 'Ground Truth',
mode = 'markers'
)
data = [trace1, trace2, trace3]
layout = Layout(
xaxis2=dict(
anchor='x2',
title='px'
),
yaxis2=dict(
anchor='y2',
title='py'
)
)
fig = Figure(data=data, layout=layout)
py.iplot(fig, filename= 'UKF')
import plotly.offline as py
from plotly.graph_objs import *
#estimations
trace1 = Scatter(
x=table_ukf_output['px_est'],
y=table_ukf_output['py_est'],
xaxis='x2',
yaxis='y2',
name='UKF- Estimate'
)
#Measurements
trace2 = Scatter(
x=table_ukf_output['px_meas'],
y=table_ukf_output['py_meas'],
xaxis='x2',
yaxis='y2',
name = 'Measurements',
#mode = 'markers'
)
#Measurements
trace3 = Scatter(
x=table_ukf_output['px_gt'],
y=table_ukf_output['py_gt'],
xaxis='x2',
yaxis='y2',
name = 'Ground Truth'
)
data = [trace1, trace2, trace3]
layout = Layout(
xaxis2=dict(
anchor='x2',
title='px'
),
yaxis2=dict(
anchor='y2',
title='py'
)
)
fig = Figure(data=data, layout=layout)
py.iplot(fig, filename= 'UKF')